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information matrix sentence in Hindi

"information matrix" meaning in Hindiinformation matrix in a sentence
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  • Where the Fisher information matrix F _ { \ theta } is the expectation of the Hessian of and renders the expression independent of the chosen parameterization.
  • To predict the information filter the information matrix and vector can be converted back to their state space equivalents, or alternatively the information space prediction can be used.
  • From the expressions for the individual components, it follows that the determinant of Fisher's ( symmetric ) information matrix for the beta distribution with four parameters is:
  • It has no walls for ?, ? ?!  " because in this case the determinant of Fisher's information matrix for the beta distribution approaches zero.
  • Finally, parameter confidence intervals may be estimated using either the Fisher information matrix approximation or based on the profile-likelihood function, if parameters are not unambiguously identifiable.
  • Harold Jeffreys proposed to use an uninformative prior probability measure that should be invariant under reparameterization : proportional to the square root of the determinant of Fisher's information matrix.
  • From the expressions for the individual components of the Fisher information matrix, it follows that the determinant of Fisher's ( symmetric ) information matrix for the beta distribution is:
  • From the expressions for the individual components of the Fisher information matrix, it follows that the determinant of Fisher's ( symmetric ) information matrix for the beta distribution is:
  • Then the Fisher information takes the form of an " N " ?" N " positive semidefinite symmetric matrix, the Fisher Information Matrix, with typical element:
  • The section titled " Geometric variance and covariance " contains plots and further discussion of the Fisher information matrix components : the log geometric variances and log geometric covariance as a function of the shape parameters ? and ?.
  • He developed the theory of the VII type Pearson distribution with the diagonal information matrix, and he proposed the method of getting of its parameters effective estimations, which is simpler, than the method proposed by Jeffreys.
  • The family of all normal distributions, parametrized by the expected value " & mu; " and the variance " ? " 2 e " 0, with the Riemannian metric given by the Fisher information matrix, is a statistical manifold.
  • When the Fisher information matrix is computed at the evaluates of the parameters ( " the observed Fisher information matrix " ) it is equivalent to the replacement of the true log likelihood surface by a Taylor's series approximation, taken as far as the quadratic terms.
  • When the Fisher information matrix is computed at the evaluates of the parameters ( " the observed Fisher information matrix " ) it is equivalent to the replacement of the true log likelihood surface by a Taylor's series approximation, taken as far as the quadratic terms.
  • The Fisher information matrix for the four parameter case does not depend on the individual values of the minimum " a " and the maximum " c ", but only on the total range ( " c "  " " a " ).
  • From Sylvester's criterion ( checking whether the diagonal elements are all positive ), it follows that the Fisher information matrix for the two parameter case is positive-definite ( under the standard condition that the shape parameters are positive ? > 0 and ? > 0 ).
  • In the Fisher information matrix, and the curvature of the log likelihood function, the logarithm of the geometric variance of the reflected variable ( 1-" X " ) and the logarithm of the geometric covariance between " X " and ( 1-" X " ) appear:
  • The Fisher information matrix component \ mathcal { I } _ { a, a } for the minimum " a " approaches infinity for exponent ? approaching 2 from above, and the Fisher information matrix component \ mathcal { I } _ { c, c } for the maximum " c " approaches infinity for exponent ? approaching 2 from above.
  • The Fisher information matrix component \ mathcal { I } _ { a, a } for the minimum " a " approaches infinity for exponent ? approaching 2 from above, and the Fisher information matrix component \ mathcal { I } _ { c, c } for the maximum " c " approaches infinity for exponent ? approaching 2 from above.
  • In general, the Fisher information matrix provides a Riemannian metric ( more precisely, the Fisher-Rao metric ) for the manifold of thermodynamic states, and can be used as an information-geometric complexity measure for a classification of phase transitions, e . g ., the scalar curvature of the thermodynamic metric tensor diverges at ( and only at ) a phase transition point.
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